{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# 导入样本数据并显示前5行信息\n",
    "df = pd.read_csv('D:\\\\python\\\\boston_house_prices.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 14)\n"
     ]
    }
   ],
   "source": [
    "# 显示样本数和特征维数\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
      "       'PTRATIO', 'B', 'LSTAT', 'MEDV'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# 输出列的名字\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索\n",
    "查看数据各特征的分布，特征与标签之间的关系，以及特征之间相关性（是否存在冗余）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boston房价预测数据集中所有特征均为数值型特征\n",
    "dectibe方法可以给出特征的基本统计学特性：为缺失的数值、均值等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677082</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677082   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 单变量分布分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#seaborn的displot方法可以对数值型特征绘制直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(df['MEDV'], bins=30, kde=True)\n",
    "plt.xlabel('Median value of owner-occupied homes', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，标签y主要集中在20附近，和正态分布比较接近。但较小的值比较密集，较大的值比较散（长尾），取值为最大值50的样本数较多（猜测是对大值做了截断），在模型训练时也可以考虑将y等于50的样本当成outliers（离群点）去掉。 纵轴为归一化后的值（概率密度）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000015F7315F2B0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000015F76878940>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用DataFrame的hist()方法也可以绘制直方图\n",
    "# distplot绘图的纵轴为样本的比例，hist方法绘图的纵轴为样本的数目\n",
    "features = ['MEDV', 'CRIM']\n",
    "df[features].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 频率表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    471\n",
       "1     35\n",
       "Name: CHAS, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurresces')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['CHAS'], order=[0,1]);\n",
    "plt.xlabel('Charles River')\n",
    "plt.ylabel('Number of occurresces')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "条形图countplot和直方图distplot看起来很像，都用于观察特征的分布，不同点：\n",
    "   \n",
    "   1.直方图用于查看数值变量的分布，而条形图用于类别特征。\n",
    "   2.直方图的X轴是数值；条形图的X轴可能是任何类型：数字、字符串、布尔值。\n",
    "   3.直方图的X轴是笛卡尔坐标轴；条形的顺序没有事先定义。不过条形经常按照高度排序，也就是值的频率。如果是有序变量，条形通常按照变量的值排序。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 两两特征之间的相关性\n",
    "\n",
    "    1.数值特征与数值特征 相关矩阵和散点图\n",
    "    2.数值特征与类别特征 \n",
    "    3.类别特征与类别特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 相关矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15f74167b38>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = df.columns\n",
    "\n",
    "# 可用DataFrame的corr()方法先计算出每对特征间的相关矩阵，然后将所得的相关矩阵传给seaborn的heatmap()方法，渲染出一个基于色彩编码的矩阵\n",
    "data_corr = df.corr()\n",
    "sns.heatmap(data_corr, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 得到相关系数的绝对值 通常相关系数大于0.5的特征为强相关\n",
    "data_corr = data_corr.abs()\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "sns.heatmap(data_corr, mask=data_corr<0.5, cbar=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x15f7c1105c0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['RM'],df['MEDV'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据分离\n",
    "\n",
    "从原始数据中分离输入特征X和输出y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['MEDV']\n",
    "X = df.drop('MEDV', axis=1)\n",
    "\n",
    "log_y = np.log1p(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 离散型特诊编码\n",
    "\n",
    "独热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  \n",
       "0  396.90   4.98  \n",
       "1  396.90   9.14  \n",
       "2  392.83   4.03  \n",
       "3  394.63   2.94  \n",
       "4  396.90   5.33  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cat = X['RAD'].astype('object')\n",
    "X_cat = pd.get_dummies(X_cat, prefix='RAD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop('RAD', axis = 1)\n",
    "feat_names = X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数值型特征预处理\n",
    "\n",
    "数值特征标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python\\lib\\site-packages\\sklearn\\preprocessing\\data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 创建实例\n",
    "ss_X = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "ss_log_y = MinMaxScaler()\n",
    "\n",
    "# 对特征和标签进行标准化处理\n",
    "X = ss_X.fit_transform(X) #首先拟合数据，然后转化它将其转化为标准形式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 保存特征工程的结果到文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_data = pd.DataFrame(data = X, columns = feat_names, index = df.index)\n",
    "fe_data = pd.concat([fe_data, X_cat], axis = 1, ignore_index=False)\n",
    "\n",
    "fe_data['MEDV'] = y\n",
    "fe_data['log_MEDV'] = log_y\n",
    "\n",
    "fe_data.to_csv('D:\\\\python\\\\FE_boston_housing.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>...</th>\n",
       "      <th>RAD_2</th>\n",
       "      <th>RAD_3</th>\n",
       "      <th>RAD_4</th>\n",
       "      <th>RAD_5</th>\n",
       "      <th>RAD_6</th>\n",
       "      <th>RAD_7</th>\n",
       "      <th>RAD_8</th>\n",
       "      <th>RAD_24</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>log_MEDV</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>...</td>\n",
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       "      <td>3.218876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.547998</td>\n",
       "      <td>0.782698</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.553191</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>21.6</td>\n",
       "      <td>3.117950</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.694386</td>\n",
       "      <td>0.599382</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.553191</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.7</td>\n",
       "      <td>3.575151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.658555</td>\n",
       "      <td>0.441813</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.648936</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.4</td>\n",
       "      <td>3.538057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.687105</td>\n",
       "      <td>0.528321</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.648936</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.2</td>\n",
       "      <td>3.616309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
       "0  0.000000  0.18  0.067815   0.0  0.314815  0.577505  0.641607  0.269203   \n",
       "1  0.000236  0.00  0.242302   0.0  0.172840  0.547998  0.782698  0.348962   \n",
       "2  0.000236  0.00  0.242302   0.0  0.172840  0.694386  0.599382  0.348962   \n",
       "3  0.000293  0.00  0.063050   0.0  0.150206  0.658555  0.441813  0.448545   \n",
       "4  0.000705  0.00  0.063050   0.0  0.150206  0.687105  0.528321  0.448545   \n",
       "\n",
       "        TAX   PTRATIO  ...  RAD_2  RAD_3  RAD_4  RAD_5  RAD_6  RAD_7  RAD_8  \\\n",
       "0  0.208015  0.287234  ...      0      0      0      0      0      0      0   \n",
       "1  0.104962  0.553191  ...      1      0      0      0      0      0      0   \n",
       "2  0.104962  0.553191  ...      1      0      0      0      0      0      0   \n",
       "3  0.066794  0.648936  ...      0      1      0      0      0      0      0   \n",
       "4  0.066794  0.648936  ...      0      1      0      0      0      0      0   \n",
       "\n",
       "   RAD_24  MEDV  log_MEDV  \n",
       "0       0  24.0  3.218876  \n",
       "1       0  21.6  3.117950  \n",
       "2       0  34.7  3.575151  \n",
       "3       0  33.4  3.538057  \n",
       "4       0  36.2  3.616309  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fe_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型训练及评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>RAD_3</th>\n",
       "      <th>RAD_4</th>\n",
       "      <th>RAD_5</th>\n",
       "      <th>RAD_6</th>\n",
       "      <th>RAD_7</th>\n",
       "      <th>RAD_8</th>\n",
       "      <th>RAD_24</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>log_MEDV</th>\n",
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       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000293</td>\n",
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       "    <tr>\n",
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       "       CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
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       "1  0.000236  0.00  0.242302   0.0  0.172840  0.547998  0.782698  0.348962   \n",
       "2  0.000236  0.00  0.242302   0.0  0.172840  0.694386  0.599382  0.348962   \n",
       "3  0.000293  0.00  0.063050   0.0  0.150206  0.658555  0.441813  0.448545   \n",
       "4  0.000705  0.00  0.063050   0.0  0.150206  0.687105  0.528321  0.448545   \n",
       "\n",
       "        TAX   PTRATIO  ...  RAD_2  RAD_3  RAD_4  RAD_5  RAD_6  RAD_7  RAD_8  \\\n",
       "0  0.208015  0.287234  ...      0      0      0      0      0      0      0   \n",
       "1  0.104962  0.553191  ...      1      0      0      0      0      0      0   \n",
       "2  0.104962  0.553191  ...      1      0      0      0      0      0      0   \n",
       "3  0.066794  0.648936  ...      0      1      0      0      0      0      0   \n",
       "4  0.066794  0.648936  ...      0      1      0      0      0      0      0   \n",
       "\n",
       "   RAD_24  MEDV  log_MEDV  \n",
       "0       0  24.0  3.218876  \n",
       "1       0  21.6  3.117950  \n",
       "2       0  34.7  3.575151  \n",
       "3       0  33.4  3.538057  \n",
       "4       0  36.2  3.616309  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('D:\\\\python\\\\FE_boston_housing.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['MEDV']\n",
    "X = df.drop(['MEDV', 'log_MEDV'], axis = 1)\n",
    "\n",
    "feat_names = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 21)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 最小二乘线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score with LR on test:  0.6974897740531418\n",
      "r2_score with LR on train:  0.7562538446521849\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print('r2_score with LR on test: ', r2_score(y_test, y_test_pred_lr))\n",
    "print('r2_score with LR on train: ', r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score with RG on test:  0.7132905097634927\n",
      "r2_score with EG on train:  0.7521099779026401\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "# 实例\n",
    "ridge = Ridge(alpha=1.0, random_state=30)\n",
    "\n",
    "# 训练\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "y_test_pred_rg = ridge.predict(X_test)\n",
    "y_train_pred_rg = ridge.predict(X_train)\n",
    "\n",
    "#r2_score评估\n",
    "print('r2_score with RG on test: ', r2_score(y_test, y_test_pred_rg))\n",
    "print('r2_score with EG on train: ', r2_score(y_train, y_train_pred_rg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score with RG on test:  0.2707661346004111\n",
      "r2_score with RG on train:  0.2314016416230794\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "lasso = Lasso(alpha=1.0, random_state=30,selection='random')\n",
    "\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "y_test_pred_la = lasso.predict(X_test)\n",
    "y_train_pred_la = lasso.predict(X_train)\n",
    "\n",
    "print('r2_score with RG on test: ', r2_score(y_test, y_test_pred_la))\n",
    "print('r2_score with RG on train: ', r2_score(y_train, y_train_pred_la))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 留一交叉验证RangeCV模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score with RGCV on test:  0.6996383764958718\n",
      "r2_score with RGCV on train:  0.756192393066\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "alphas = [0.01, 0.1, 1, 10, 100]\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "y_test_pred_rgcv = ridge.predict(X_test)\n",
    "y_train_pred_rgcv = ridge.predict(X_train)\n",
    "\n",
    "print('r2_score with RGCV on test: ', r2_score(y_test, y_test_pred_rgcv))\n",
    "print('r2_score with RGCV on train: ', r2_score(y_train, y_train_pred_rgcv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "RidgeCV 模型比较适合特征建存在共线性问题的求解"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 留一交叉验证LassoCV模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_score with LACV on test:  0.6980124956825822\n",
      "r2_score with LACV on train:  0.7562255079772654\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "lasso = LassoCV(cv=3)\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "y_test_pred_lacv = lasso.predict(X_test)\n",
    "y_train_pred_lacv = lasso.predict(X_train)\n",
    "\n",
    "print('r2_score with LACV on test: ', r2_score(y_test, y_test_pred_lacv))\n",
    "print('r2_score with LACV on train: ', r2_score(y_train, y_train_pred_lacv))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasso 模型比较适合特征数量较少 即稀疏矩阵的求解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is:  0.0013884165768061959\n"
     ]
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis=1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses)\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print('alpha is: ', lasso.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
